EDA On Data Science Job Salaries Dataset¶

Table of Content¶

I. Notebook Objectives¶

II. What is Data Science¶

III. Import Library¶

IV. Summaries Data Set¶

V. Data Analysis¶

VI. Import New Data Set¶

VII. Conclusion¶

VIII. References¶

I. Notebook Objectives¶

The objective of this notebook is to analyze and investigate salary trends in the field of data science across different positions and geographical locations worldwide. By examining salary data obtained from ai-jobs.net, we aim to gain insights into the factors influencing salary variations and understand how data analysis techniques can be utilized to extract meaningful insights from the dataset.¶

Specific Objectives:¶

- Understand how to analyze data to derive meaningful insights and trends.¶

- Investigate salary distributions and variations across different positions within the field of data science.¶

- Explore geographical differences in salary trends for data science positions around the world.¶

- Utilize data visualization techniques to present findings and enhance understanding.¶

Note: This notebook is intended for educational purposes and skill improvement. It should not be considered as formal research or a professional paper.¶

II. What is Data Science¶

Data science is a field that uses a mix of methods, algorithms, and tools to understand and make sense of different types of data. It combines ideas from statistics, math, computer science, and specific areas of expertise to analyze and interpret complex data sets. The data science lifecycle begins with business understanding, followed by data mining, data cleaning, exploration, advanced analytics, and insights visualization.¶

Data science is also pivotal for modern organizations, facilitating informed decision-making through data-driven insights and predictive analytics. It empowers businesses to anticipate trends and risks, driving proactive strategies. By transforming raw data into actionable insights, data science enhances business intelligence, fostering a deeper understanding of operations, customers, and markets for improved competitiveness.¶

Due to their significant value to organizations, data science positions are characterized by high demand and high compensation. In light of this, we aim to conduct an in-depth analysis of data science salaries spanning the years 2021 to 2024, to examine trends and patterns through comprehensive data analysis and visualization techniques¶

III. Import library¶

In [1]:
# Import image 
from IPython.display import Image
# Data analysis 
import pandas as pd
pd.plotting.register_matplotlib_converters() 
import numpy as np

# Visualization
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline 
import seaborn as sns
import plotly.express as px
import datetime as dt
import warnings
import plotly.graph_objects as go

IV. Summaries Dataset¶

In [2]:
df=pd.read_csv('/Users/thienphuquach/Desktop/Research Paper/ds_salaries.csv')
df.head(10)
Out[2]:
Unnamed: 0 work_year experience_level employment_type job_title salary salary_currency salary_in_usd employee_residence remote_ratio company_location company_size
0 0 2020 MI FT Data Scientist 70000 EUR 79833 DE 0 DE L
1 1 2020 SE FT Machine Learning Scientist 260000 USD 260000 JP 0 JP S
2 2 2020 SE FT Big Data Engineer 85000 GBP 109024 GB 50 GB M
3 3 2020 MI FT Product Data Analyst 20000 USD 20000 HN 0 HN S
4 4 2020 SE FT Machine Learning Engineer 150000 USD 150000 US 50 US L
5 5 2020 EN FT Data Analyst 72000 USD 72000 US 100 US L
6 6 2020 SE FT Lead Data Scientist 190000 USD 190000 US 100 US S
7 7 2020 MI FT Data Scientist 11000000 HUF 35735 HU 50 HU L
8 8 2020 MI FT Business Data Analyst 135000 USD 135000 US 100 US L
9 9 2020 SE FT Lead Data Engineer 125000 USD 125000 NZ 50 NZ S

work_year: The year the salary was paid.¶

experience_level: Experience level at the job during the year¶

employment_type: Type of employment (PT-Part-time, FT-Full-time, CT-Contract, FL-Freelance)¶

job_title: Job role during the year¶

salary: Total gross salary amount¶

salary_currency: Currency of salary, paid as an ISO 4217 currency code¶

salary_in_usd: Salary in USD¶

employee_residence: Employee's primary country of residence, as an ISO 3166 country code¶

remote_ratio: Overall amount of work done remotely¶

company_location: Country of employer's main office or contracting branch, as an ISO 3166 country code¶

company_size: Amount of people worked for the company¶

In [3]:
# Checking the type of each 
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 607 entries, 0 to 606
Data columns (total 12 columns):
 #   Column              Non-Null Count  Dtype 
---  ------              --------------  ----- 
 0   Unnamed: 0          607 non-null    int64 
 1   work_year           607 non-null    int64 
 2   experience_level    607 non-null    object
 3   employment_type     607 non-null    object
 4   job_title           607 non-null    object
 5   salary              607 non-null    int64 
 6   salary_currency     607 non-null    object
 7   salary_in_usd       607 non-null    int64 
 8   employee_residence  607 non-null    object
 9   remote_ratio        607 non-null    int64 
 10  company_location    607 non-null    object
 11  company_size        607 non-null    object
dtypes: int64(5), object(7)
memory usage: 57.0+ KB
In [4]:
# Convert the "remote_ratio" column to text
df['remote_ratio'] = df['remote_ratio'].astype(str)

# Verify the changes
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 607 entries, 0 to 606
Data columns (total 12 columns):
 #   Column              Non-Null Count  Dtype 
---  ------              --------------  ----- 
 0   Unnamed: 0          607 non-null    int64 
 1   work_year           607 non-null    int64 
 2   experience_level    607 non-null    object
 3   employment_type     607 non-null    object
 4   job_title           607 non-null    object
 5   salary              607 non-null    int64 
 6   salary_currency     607 non-null    object
 7   salary_in_usd       607 non-null    int64 
 8   employee_residence  607 non-null    object
 9   remote_ratio        607 non-null    object
 10  company_location    607 non-null    object
 11  company_size        607 non-null    object
dtypes: int64(4), object(8)
memory usage: 57.0+ KB
In [5]:
df['remote_ratio'].replace({'0':'No remote','50':'Partially remote','100':' Fully remote'},inplace=True)
df['company_size'].replace({'L':'Large','S':'Small','M':'Medium'},inplace=True)
df=df.drop('Unnamed: 0',axis=1)
df
Out[5]:
work_year experience_level employment_type job_title salary salary_currency salary_in_usd employee_residence remote_ratio company_location company_size
0 2020 MI FT Data Scientist 70000 EUR 79833 DE No remote DE Large
1 2020 SE FT Machine Learning Scientist 260000 USD 260000 JP No remote JP Small
2 2020 SE FT Big Data Engineer 85000 GBP 109024 GB Partially remote GB Medium
3 2020 MI FT Product Data Analyst 20000 USD 20000 HN No remote HN Small
4 2020 SE FT Machine Learning Engineer 150000 USD 150000 US Partially remote US Large
... ... ... ... ... ... ... ... ... ... ... ...
602 2022 SE FT Data Engineer 154000 USD 154000 US Fully remote US Medium
603 2022 SE FT Data Engineer 126000 USD 126000 US Fully remote US Medium
604 2022 SE FT Data Analyst 129000 USD 129000 US No remote US Medium
605 2022 SE FT Data Analyst 150000 USD 150000 US Fully remote US Medium
606 2022 MI FT AI Scientist 200000 USD 200000 IN Fully remote US Large

607 rows × 11 columns

In [6]:
# Replace Acronym 
df['experience_level'].replace({'EN':'Entry Level','MI':'Intermediate','EX':'Expert','SE':'Senior'},inplace=True)
df['employment_type'].replace({'PT':'Part-Time','FT':'Full-Time','CT':'Contract','FL':'Freelance'},inplace=True)
df
Out[6]:
work_year experience_level employment_type job_title salary salary_currency salary_in_usd employee_residence remote_ratio company_location company_size
0 2020 Intermediate Full-Time Data Scientist 70000 EUR 79833 DE No remote DE Large
1 2020 Senior Full-Time Machine Learning Scientist 260000 USD 260000 JP No remote JP Small
2 2020 Senior Full-Time Big Data Engineer 85000 GBP 109024 GB Partially remote GB Medium
3 2020 Intermediate Full-Time Product Data Analyst 20000 USD 20000 HN No remote HN Small
4 2020 Senior Full-Time Machine Learning Engineer 150000 USD 150000 US Partially remote US Large
... ... ... ... ... ... ... ... ... ... ... ...
602 2022 Senior Full-Time Data Engineer 154000 USD 154000 US Fully remote US Medium
603 2022 Senior Full-Time Data Engineer 126000 USD 126000 US Fully remote US Medium
604 2022 Senior Full-Time Data Analyst 129000 USD 129000 US No remote US Medium
605 2022 Senior Full-Time Data Analyst 150000 USD 150000 US Fully remote US Medium
606 2022 Intermediate Full-Time AI Scientist 200000 USD 200000 IN Fully remote US Large

607 rows × 11 columns

In [7]:
#Checking for null values
df.isnull().sum()
Out[7]:
work_year             0
experience_level      0
employment_type       0
job_title             0
salary                0
salary_currency       0
salary_in_usd         0
employee_residence    0
remote_ratio          0
company_location      0
company_size          0
dtype: int64

V. Data Analysis¶

1) Experience Level¶

In [8]:
# Group the DataFrame by 'experience_level' and calculate the count of job listings
experience_counts = df.groupby('experience_level', as_index=False)['salary_in_usd'].count().sort_values(by='salary_in_usd', ascending=False)

# Create the pie chart using Plotly Express
fig = px.pie(
    experience_counts,
    names='experience_level',
    values='salary_in_usd',
    color='experience_level',
    hole=0.4,
    labels={'experience_level': 'Experience Level', 'salary_in_usd': 'Count'},
    template='plotly_dark',
    title='<b>Total Jobs Based on Experience Level'
)

# Update the layout to adjust title position and legend orientation
fig.update_layout(
    title_x=0.5,
    legend=dict(
        orientation='h',
        yanchor='bottom',
        y=1.02,
        xanchor='right',
        x=0.8, 
       
    )
)

# Show the pie chart
fig.show()

In this dataset, as seen from the treemap above, it's evident that Senior and Intermediate levels together make up more than 80%, while entry and expert levels account for only 14.5% and 4.28%, respectively.¶

The predominance of Senior and Intermediate levels in this dataset may be attributed to several factors. Primarily, a significant proportion of individuals in these roles hold advanced degrees, such as Master's and PhDs, which are typically associated with positions beyond entry-level.¶

Drawing from the methodology employed by Rosanne Bollen in "Competencies of the Sexiest Job of the 21st Century: Developing and Testing a Measurement Instrument for Data Scientists," our analysis involves a quantitative cross-sectional questionnaire research to assess the validity of our revised competency framework (Bollen, Competencies of the Sexiest Job of the 21st Century: Developing and Testing a Measurement Instrument for Data Scientists 2017). Examining Sample 1, it is notable that 68.4% of respondents possess a Master's degree, while 6.6% have attained a PhD or other advanced degree, indicating a high level of educational attainment within the field. Additionally, age and experience play a significant role, with individuals aged 26-30, 31-35, and 36-40 collectively constituting over 70% of the sample.¶

2) Popular Role in Data Science¶

In [9]:
z=df['job_title'].value_counts().head(15)
fig=px.bar(z,x=z.index,
           y=z.values,
           color=z.index,
           text=z.values,
           labels={'index':'Job Title','y':'total','text':'count'},
           template='plotly_dark',
           title='<b> Top Popular Roles in Data Science')
fig.update_layout(title_x=0.5)
fig.show()

The demand for Data Scientists, Data Engineers, and Data Analysts is evidently high worldwide. Particularly in the United States, as indicated by research such as "A Data Science Approach to Defining a Data Scientist" by Andy Ho, data collected from Indeed in 16 major American cities revealed approximately 8,738 job postings for these roles. It's worth noting that this dataset does not encompass other hiring platforms like LinkedIn. Within this dataset, there were nearly 1,742 positions specifically for Data Scientists.(Ho et al., A Data Science Approach to Defining a Data Scientist 2019)¶

2) Salaries in Data Science field¶

In [10]:
fig=px.bar(df.groupby('job_title',as_index=False)['salary_in_usd'].max().sort_values(by='salary_in_usd',
            ascending=False).head(10),
           x='job_title',
           y='salary_in_usd',
           color='job_title',
           labels={'job_title':'job title','salary_in_usd':'salary in usd'},
           template='plotly_dark',
           text='salary_in_usd',
           title='<b> Top 10 Highest Paid Roles in Data Science')
fig.update_layout(title_x=0.5)
fig.show()
In [11]:
sns.scatterplot(x=df.index, y='salary_in_usd', data=df, color='orange', alpha=0.6, label='Salary')
sns.regplot(x=df.index, y='salary_in_usd', data=df, scatter=False, color='red', label='Regression line')

# Customize the plot
plt.title('Salary Distribution')
plt.xlabel('Person')
plt.ylabel('Salary (USD)')
plt.legend()  # Show legend with labels
plt.grid(True)  # Add gridlines
plt.tight_layout()  # Adjust layout to prevent overlapping labels
plt.show()
No description has been provided for this image

4) Remote ratio¶

In [12]:
fig=px.pie(df.groupby('remote_ratio',as_index=False)['salary_in_usd'].count().sort_values(by='salary_in_usd',ascending=False),
           names='remote_ratio',
           values='salary_in_usd',
           color='remote_ratio',
           hole=0.5,
           labels={'remote_ratio':'remote ratio','salary_in_usd':'count'},
           template='plotly_dark',
           title='<b> Remote Ratio')
fig.update_layout(
    title_x=0.5,
    legend=dict(
        yanchor='bottom',
        y=0.4,
        xanchor='right',
        x=0.8, 
    )
)

After the COVID-19 pandemic, many companies have embraced remote work, especially in the IT/Data field where it's often feasible. This shift has given organizations ample opportunities to redefine their programming teams' work environment. They can tailor everything from office layout ergonomics to upgrading infrastructure and providing better tools, all while enhancing flexibility in work hours and formats. Determining which factors truly influence team efficiency and effectiveness can be challenging. According to this dataset, approximately 62.8% of people are now working fully remotely, compared to just around 21% who continue to work on-site.(Rot et al., Programming teams in Remote Working Environments: An analysis of performance and productivity 2023)¶

In [13]:
Image(filename='/Users/thienphuquach/Desktop/Research Paper/DS/Remote ratio by experience.png')
Out[13]:
No description has been provided for this image

5) Company Size¶

                            ### Figure 1: Remote ratio by experience level (Created by Phu Quach)

Despite the substantial prevalence of remote work arrangements, recent research, such as that conducted by Artur Rot, Malgorzata Sobinska and Peter Busch in "Programming Teams in Remote Working Environments: an Analysis of Performance and Productivity," highlights a noteworthy finding. Namely, it reveals the apparent absence or marginal negative impact of remote work on the operational efficiency of development teams.¶

However, junior developers manifest a distinct sentiment, characterized by a perceived constraint in articulating and resolving issues collaboratively, an activity traditionally facilitated through in-person interaction at the monitor or whiteboard. This phenomenon suggests a plausible link to their relative lack of experience and depth of domain-specific knowledge compared to their senior counterparts, thus potentially inhibiting their seamless integration into remote work paradigms.(Rot et al., Programming teams in Remote Working Environments: An analysis of performance and productivity 2023)¶

In [14]:
fig=px.pie(df.groupby('company_size',as_index=False)['salary_in_usd'].count().sort_values(by='salary_in_usd',ascending=False).head(10),
           names='company_size',
           values='salary_in_usd',
           color='company_size',
           hole=0.5,labels={'company_size':'Company Size','salary_in_usd':'count'},
           template='plotly_dark',
           title='<b> Company Sizes in Data Science Feild')
fig.update_layout(title_x=0.5,legend=dict(orientation='h',yanchor='bottom',y=1.02,xanchor='right',x=1))

In the contemporary digital landscape, data has risen to prominence as a pivotal asset for businesses, shaping decision-making processes, fostering innovation, and propelling growth. The deployment of data science solutions, entailing the extraction of valuable insights from extensive datasets, has yielded substantial impacts on company revenues.¶

In the contemporary landscape of e-commerce, the convergence of big data and Customer Relationship Management (CRM) has catalyzed a significant paradigm shift. This transformative integration is not exclusive to large tech conglomerates but also extends its potential to medium and small-sized enterprises. CRM, a strategic concept devised to bolster companies' profitability, empowers organizations to pinpoint and prioritize their most lucrative customer segments. Particularly within the realm of electronic commerce, where Electronic Customer Relationship Management (e-CRM) reigns supreme, the application of CRM principles emerges as a linchpin for success. This holds especially true for business-to-consumer entities and smaller firms, which, despite lacking the vast resources of corporate giants, are compelled to adeptly manage customer relationships to thrive in the digital marketplace. Contrary to conventional wisdom equating a company's tenure on the web with its electronic commerce prowess, the authors posit that success hinges more significantly on the adept execution of CRM strategies. Thus, irrespective of a company's size or longevity in the digital sphere, the effective deployment of CRM methodologies emerges as the cornerstone for sustained competitiveness and growth.¶

Example based on the analysis¶

For mid-cap companies, a deeper examination of e.l.f. Beauty provides illuminating insights(e.l.f. Beauty, Inc, Annual report 2023). In the fiscal year ending March 31, 2023, the company witnessed a remarkable surge in net sales, soaring by 186.6 million dollars, marking a staggering 48 percent increase from the previous fiscal year's 392.2 million to 578.8 million dollars . This substantial growth was primarily fueled by robust performance across both retail and e-commerce channels. Notably, net sales surged by 158.0 million dollars, a notable 45%, within the retailer channels, while e-commerce channels experienced a remarkable $28.6 million increase, representing an impressive 71% spike. This exemplifies the efficacy of data collection and analysis methodologies employed by smaller companies to optimize revenue streams. Moreover, e.l.f. Beauty underscores the pivotal role of its e-commerce operations within its business model. Their e-commerce platforms serve as integral extensions of their marketing strategies, acting as vital touchpoints for introducing prospective consumers to their brand, product offerings, and enriched content.However, amidst these opportunities lie inherent vulnerabilities. The reliance on e-commerce platforms exposes the company to risks such as website downtime and technical failures. A failure to swiftly address these challenges could potentially curtail e-commerce sales and tarnish the brand's reputation. Thus, it becomes imperative for companies like e.l.f. Beauty to proactively mitigate such risks to ensure sustained growth and brand resilience in the digital sphere.¶

For the leading entity in the e-commerce domain, Amazon unequivocally stands out as the preeminent corporation, boasting a colossal user base numbering in the billions. According to research on Big Data Techniques of Google, Amazon, Facebook, and Twitter, Amazon currently hosts over 300 million active users (Hewage et al., Review: Big data techniques of google, Amazon, Facebook and Twitter 2018). Within this expansive dataset lies a profound emphasis on Customer Relationship Management (CRM), underlining its critical significance within electronic commerce. Amazon's strategic utilization of big data techniques for CRM exemplifies a sophisticated approach to managing customer interactions and data, serving as a benchmark within the industry. With an intricate understanding of consumer behavior and preferences gleaned from extensive data analysis, Amazon adeptly tailors its services to individual users. This personalized approach manifests in targeted marketing initiatives, precise product recommendations, and refined customer segmentation strategies.Moreover, Amazon's mastery of big data extends beyond CRM, encompassing pivotal functions such as supply chain optimization, inventory management, fraud prevention, and logistical enhancements. By harnessing its robust infrastructure and cutting-edge analytics capabilities, Amazon processes vast data volumes in real-time, facilitating agile decision-making and fostering ongoing innovation.¶

In [15]:
Image(filename='/Users/thienphuquach/Desktop/Research Paper/DS/dashboard2.png')
Out[15]:
No description has been provided for this image 
 ## Figure 1 : Tableau Dashboard Data Science Salary around the world by USD from 2020 to 2023 (Created by Phu Quach)

Following in-depth analysis, it's evident that data science has achieved global prominence, particularly in regions such as Europe, North America, Austria, and select areas in Asia. This widespread recognition underscores the escalating demand for expertise in data-related fields, indicating a substantial surge in their significance. This trend accentuates the growing prominence of data science across diverse industries and sectors.¶

VII. Import New Dataset¶

In [16]:
df2=pd.read_csv('/Users/thienphuquach/Desktop/salaries.csv')
df2
Out[16]:
work_year experience_level employment_type job_title salary salary_currency salary_in_usd employee_residence remote_ratio company_location company_size
0 2023 MI FT Prompt Engineer 56000 EUR 60462 DE 0 DE S
1 2024 EN FT Research Engineer 172800 USD 172800 US 0 US M
2 2024 EN FT Research Engineer 144000 USD 144000 US 0 US M
3 2024 MI FT Data Engineer 65000 EUR 72222 AT 0 AT M
4 2024 MI FT Data Engineer 43000 EUR 47777 AT 0 AT M
... ... ... ... ... ... ... ... ... ... ... ...
12977 2020 SE FT Data Scientist 412000 USD 412000 US 100 US L
12978 2021 MI FT Principal Data Scientist 151000 USD 151000 US 100 US L
12979 2020 EN FT Data Scientist 105000 USD 105000 US 100 US S
12980 2020 EN CT Business Data Analyst 100000 USD 100000 US 100 US L
12981 2021 SE FT Data Science Manager 7000000 INR 94665 IN 50 IN L

12982 rows × 11 columns

In [17]:
df2['company_size'].replace({'L':'Large','S':'Small','M':'Medium'},inplace=True)
df2['experience_level'].replace({'EN':'Entry Level','MI':'Intermediate','EX':'Expert','SE':'Senior'},inplace=True)
df2['employment_type'].replace({'PT':'Part-Time','FT':'Full-Time','CT':'Contract','FL':'Freelance'},inplace=True)
df2['remote_ratio'] = df2['remote_ratio'].astype(str)
df2['remote_ratio'].replace({'0':'No remote','50':'Partially remote','100':' Fully remote'},inplace=True)

df2
Out[17]:
work_year experience_level employment_type job_title salary salary_currency salary_in_usd employee_residence remote_ratio company_location company_size
0 2023 Intermediate Full-Time Prompt Engineer 56000 EUR 60462 DE No remote DE Small
1 2024 Entry Level Full-Time Research Engineer 172800 USD 172800 US No remote US Medium
2 2024 Entry Level Full-Time Research Engineer 144000 USD 144000 US No remote US Medium
3 2024 Intermediate Full-Time Data Engineer 65000 EUR 72222 AT No remote AT Medium
4 2024 Intermediate Full-Time Data Engineer 43000 EUR 47777 AT No remote AT Medium
... ... ... ... ... ... ... ... ... ... ... ...
12977 2020 Senior Full-Time Data Scientist 412000 USD 412000 US Fully remote US Large
12978 2021 Intermediate Full-Time Principal Data Scientist 151000 USD 151000 US Fully remote US Large
12979 2020 Entry Level Full-Time Data Scientist 105000 USD 105000 US Fully remote US Small
12980 2020 Entry Level Contract Business Data Analyst 100000 USD 100000 US Fully remote US Large
12981 2021 Senior Full-Time Data Science Manager 7000000 INR 94665 IN Partially remote IN Large

12982 rows × 11 columns

In [18]:
combined_df = pd.concat([df, df2], ignore_index=True)
combined_df
Out[18]:
work_year experience_level employment_type job_title salary salary_currency salary_in_usd employee_residence remote_ratio company_location company_size
0 2020 Intermediate Full-Time Data Scientist 70000 EUR 79833 DE No remote DE Large
1 2020 Senior Full-Time Machine Learning Scientist 260000 USD 260000 JP No remote JP Small
2 2020 Senior Full-Time Big Data Engineer 85000 GBP 109024 GB Partially remote GB Medium
3 2020 Intermediate Full-Time Product Data Analyst 20000 USD 20000 HN No remote HN Small
4 2020 Senior Full-Time Machine Learning Engineer 150000 USD 150000 US Partially remote US Large
... ... ... ... ... ... ... ... ... ... ... ...
13584 2020 Senior Full-Time Data Scientist 412000 USD 412000 US Fully remote US Large
13585 2021 Intermediate Full-Time Principal Data Scientist 151000 USD 151000 US Fully remote US Large
13586 2020 Entry Level Full-Time Data Scientist 105000 USD 105000 US Fully remote US Small
13587 2020 Entry Level Contract Business Data Analyst 100000 USD 100000 US Fully remote US Large
13588 2021 Senior Full-Time Data Science Manager 7000000 INR 94665 IN Partially remote IN Large

13589 rows × 11 columns

In [19]:
px.violin(combined_df,
          x='work_year',
          y='salary_in_usd',
          color='work_year',
          labels={'work_year':'Year','salary_in_usd':'salary in usd'},
          template='plotly_dark',
          title='<b>Data Science Salaries by year')
In [20]:
Image(filename='/Users/thienphuquach/Desktop/Research Paper/DS/Dashboard1.PNG')
Out[20]:
No description has been provided for this image 
   Figure 2 : Tableau Dashboard Data Science Salary around the world by USD between 2023 and 2024 (Created by Phu Quach)

X.Conclusion¶

The period spanning from 2020 to 2024 has been characterized by substantial advancements and transformative shifts within the realm of data analysis and data science. Throughout these years, discernible trends have emerged, delineating a trajectory of progress and innovation within the field.Firstly, there has been a continued rise in demand for skilled professionals in data analysis and data science roles. Companies have recognized the importance of leveraging data to gain insights, optimize processes, and drive strategic decision-making.¶

As a result, there has been a steady increase in job opportunities and career prospects for individuals with expertise in data analysis, machine learning, and artificial intelligence. Additionally, advancements in technology and tools have empowered data scientists to extract deeper insights from complex datasets. The adoption of cloud computing, big data platforms, and automated machine learning solutions has facilitated faster data processing and model development, enabling organizations to derive value from their data more efficiently. Moreover, the integration of data science into various industries, including finance, healthcare, retail, and manufacturing, has led to transformative changes in business operations and customer experiences. Companies have leveraged predictive analytics, recommendation systems, and personalized marketing strategies to enhance customer engagement, improve product offerings, and drive revenue growth. Furthermore, the emphasis on data privacy and ethical considerations has become increasingly important in the data science community. With the implementation of regulations such as GDPR and the growing awareness of data ethics, organizations have prioritized the responsible collection, storage, and usage of data to maintain trust and transparency with customers and stakeholders.¶

Looking ahead, the future of data analysis and data science holds immense potential for innovation and impact. As organizations continue to invest in data-driven initiatives and embrace emerging technologies such as artificial intelligence, machine learning, and predictive analytics, the role of data scientists will become even more critical in driving business success and societal progress.¶

VII. References¶

1.Bollen, R. (2017). Competencies of the Sexiest Job of the 21st Century: Developing and Testing a Measurement Instrument for Data Scientists .¶

2.Cristescu, M. P., Mara, D. A., Culda, L. C., Nerișanu, R. A., Bâra, A., & Oprea, S.-V. (2023). The Impact of Data Science Solutions on the Company Turnover. https://doi.org/10.20944/preprints202309.0560.v1¶

3.e.l.f. Beauty, Inc. (2023, March 31). Annual report 2023 . https://investor.elfbeauty.com/stock-and-financial/latest-annual-report-and-proxy-statement¶

4.Hewage, T. N., Halgamuge, M. N., Syed, A., & Ekici, G. (2018). Review: Big data techniques of google, Amazon, Facebook and Twitter. Journal of Communications, 94–100. https://doi.org/10.12720/jcm.13.2.94-100¶

5.Ho, A., Nguyen, A., Pafford, J., & Slater, R. (2019). A Data Science Approach to Defining a Data Scientist. SMU Data Science Review, 2.¶

6.Rot, A., Sobinska, M., & Busch, P. (2023). Programming teams in Remote Working Environments: An analysis of performance and productivity. 2023 13th International Conference on Advanced Computer Information Technologies (ACIT). https://doi.org/10.1109/acit58437.2023.10275614¶